Plot and fit distributions of variables

Histogram of Velocity of all
Log distribution, 0’s make up 3% of all data
CC.TotalData <- na.omit(TotalData)
head(CC.TotalData)
10^mean(log10(CC.TotalData$v[CC.TotalData$v>0]))
[1] 0.0348809
sd(log10(CC.TotalData$v[CC.TotalData$v>0]))
[1] 0.5467087
Now we’re going to plot each unique krill and their bimodality with a dip test (less than 0.05 is multimodality)

Starting to look at bimodality in the variables by merging the TotalData frame with the “tab” table


plot(tab_AGG$Flow.Rate, tab_AGG$dip.test, xlab = "Flow Rate (cm/s)", ylab = "Dip Test (p.value)")
plot(tab_AGG$Flow.Rate, tab_AGG$skew, xlab = "Flow Rate (cm/s)", ylab = "Skew Test (p.value)")
plot(tab_AGG$Chlorophyll, tab_AGG$dip.test, xlab = "Chlorophyll (mg/L)", ylab = "Dip Test (p.value)")

plot(tab_AGG$Chlorophyll, tab_AGG$skew, xlab = "Chlorophyll (mg/L)", ylab = "Skew Test (p.value)")

Now looking at turning angles


for (i in 1:length(ind)){
plot(CC.TotalData$turn.angle[CC.TotalData$D_V_T==ind[i]], log10(CC.TotalData$v[CC.TotalData$D_V_T==ind[i]]),
xlab = "Turn Angles",
ylab = "Velocity",
main = "")
}




























































































































NA

save.image("~/Post-doc/Data/Total Merged Data File.RData")
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